This paper proposes a new nature-inspired metaheuristic algorithm called Clouded Leopard optimization (CLO), which mimics the natural behavior of clouded leopards in the wild. The fundamental inspiration of CLO is der...
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This paper proposes a new nature-inspired metaheuristic algorithm called Clouded Leopard optimization (CLO), which mimics the natural behavior of clouded leopards in the wild. The fundamental inspiration of CLO is derived from two ways of natural behaviors of the clouded leopard, including hunting strategy and daily resting on trees. CLO is mathematically modeled in two phases of exploration and exploitation, based on the simulation of these two natural behaviors. CLO performance is evaluated in solving sixty-eight benchmark functions, including unimodal, multimodal, CEC 2015, and CEC 2017 types. The performance of CLO in solving optimization problems is compared with the performance of ten famous metaheuristic algorithms. The simulation results show that the proposed CLO approach with high ability in exploration, exploitation, and balancing between them has a high capability in optimization applications. Simulation results show that CLO performs better in most test functions than competitor algorithms. In addition, the implementation of CLO on four engineering design issues demonstrates the capability of the proposed approach in real-world applications.
Fire disaster is one of the most dangerous disasters in the utility tunnel with plenty of high-voltage and communication cables. Fire source identification is an important part of fire protection in utility tunnel fir...
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Fire disaster is one of the most dangerous disasters in the utility tunnel with plenty of high-voltage and communication cables. Fire source identification is an important part of fire protection in utility tunnel fires. The particle swarm optimization (PSO) algorithm based on limited temperature observations was applied in the multiple fire sources identification problem, and a constrained PSO algorithm is developed for performance improvement. The fire characteristics could be estimated simultaneously, including the fire source location, the maximum temperature value, and the attenuation coefficient. Based on these parameters, the whole temperature distribution of the tunnel could be predicted correspondingly. The feasibility, superiority, and robustness of the proposed algorithm were demonstrated in numerical and experimental scenarios. Results showed that the proposed constrained algorithm could identify the double fire sources with high accuracy, and the identified locations were gathered around the actual ones in comparison with the basic algorithm. The fire source locations and fire states could be estimated under noisy and disturbance situations within an acceptance error. When the measurement noises varied from 0.02 to 0.10, the temperature prediction error of each measurement point changed from [0.1 degrees C, 5.4 degrees C] to [7.3 degrees C, 36.8 degrees C]. Additionally, the closer the distance between fire source and sensors is, and the more sensors allocated, the higher the prediction accuracy is.
In the present study, dependable and accurate variables of solid oxide fuel cell (SOFC) are identified using an accurate and remarkable convergence pace optimization algorithm, socalled cuckoo search grey wolf optimiz...
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In the present study, dependable and accurate variables of solid oxide fuel cell (SOFC) are identified using an accurate and remarkable convergence pace optimization algorithm, socalled cuckoo search grey wolf optimization (CSGWO). The mentioned hybrid algorithm is acquired from the integration of cuckoo search (CS) and grey wolf optimization (GWO) algorithms. To confirm the CSGWO performance, it is compared with well-known optimization algorithms. The proposed CSGWO hybrid algorithm is applied for a 5-kW physically-based dynamic tubular stack. Along with this, the modeling is performed for different temperatures and pressures. The results of the CSGWO integrated algorithm reveal the supremacy of the considered method compared to other algorithms with the lowest values of MSE and corroborate precision, robustness, and excellent convergence pace compared to different optimization algorithms. The statistical results revealed that the developed CSGWO algorithm has the lowest MSE values by 1.3%, 0.1%, 0.3%, 0.1%, and 0% for the operational pressures of 1, 2, 3, 4, and 5 atm, respectively. All in all, the experimental records confirm that the CSGWO integrated algorithm could be introduced as a favorable substitute to the SOFC models' parameter estimation. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
This paper proposes a new nature-based metaheuristic algorithm called Fennec Fox optimization (FFA), mimicking two natural behaviors of the animal Fennec Fox in nature. Concretely, Fennec's digging ability and esc...
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This paper proposes a new nature-based metaheuristic algorithm called Fennec Fox optimization (FFA), mimicking two natural behaviors of the animal Fennec Fox in nature. Concretely, Fennec's digging ability and escape strategy from wild predators were the fundamental inspiration for the proposed FFA. The mathematical model of FFA is presented in two phases based on imitating these two behaviors. First, the efficiency of FFA was evaluated in the optimization of sixty-eight standard benchmark functions and four engineering design problems. Second, FFA performance is challenged against eight well-known optimization algorithms. The optimization results show that FFA perfectly balances exploration and exploitation in searching for the global optimum. Hence, FFA can provide suitable solutions to optimization problems. The comparison of results indicates the superiority of FFA in most objective functions over competitor algorithms in providing the optimal solution.
Due to the rapid development of photovoltaic (PV) system and spreading of its application, the accuracy of modeling of solar cells, as the main and basic element of PV systems, is gaining relevance. In this paper, an ...
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Due to the rapid development of photovoltaic (PV) system and spreading of its application, the accuracy of modeling of solar cells, as the main and basic element of PV systems, is gaining relevance. In this paper, an Enhanced Harris Hawk optimization algorithm (EHHO) is proposed and applied for estimating the required parameters of different PV models in an effective and accurate way. Harris Hawk algorithm (HHO) is based on Hawks ways in hunting and catching their preys. The HHO utilizes two phases including exploration and exploitation. The main purpose of proposed enhancement is to improve the second phase of HHO. This enhancement is performed on the exploration phase by fluctuating toward or outward the best optimal solution using sine and cosine functions. Both conventional and proposed algorithms are applied for single, double and triple diode PV models. In order to test the applicability and robustness of proposed algorithm, it is applied for estimating the parameters of different real PV systems and compared with other recent optimization algorithms. The results show that the proposed EHHO is more accurate than conventional HHO and other algorithms.
The requirement to solve the problem of Inverse Kinetics (IK) plays a very important role in the robotics field in general, and especially in the field of rehabilitation robots, in particular. If the solutions of this...
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The requirement to solve the problem of Inverse Kinetics (IK) plays a very important role in the robotics field in general, and especially in the field of rehabilitation robots, in particular. If the solutions of this problem are not suitable, it can cause undesirable damage to the patient when exercising. Normally, the problem of Inverse Kinematics in the robotics field, as well as the natural field, especially for redundant driven systems, often requires the application of a lot of techniques. The redundancy in Degree of Freedom (DoF), the nonlinearity of the system leads to solve inverse kinematics problem more challenge. In this study, we proposed to apply the self-adaptive control parameters in Differential Evolution with search space improvement (Pro-ISADE) to solve the problem for the human upper limb, which is a very typical redundancy model in nature. First of all, the angles of the joints were measured by a proposed Exoskeleton type Human Motion Capture System (E-HMCS) when the wearer performs some Activities of Daily Living (ADL) and athletic activities. The values of these measured angles joints then were put into the forward kinematics model to find the end effector trajectories. After having these orbits, they were re-fed into the proposed Pro-ISADE algorithm mentioned above to process the IK problem and obtain the predicted joints angular values. The experimental results showed that the predicted joints' values closely follow the measured joints' values. That demonstrates the ability to apply the Pro-ISADE algorithm to solve the problem of Inverse Kinetics of the human upper limb as well as the upper limb rehabilitation robot arm.
In everyday life, the Wireless Sensor Network has attained high demand increasingly since it provides more network structure to create various kinds of innovative real-time applications. One of the essential applicati...
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In everyday life, the Wireless Sensor Network has attained high demand increasingly since it provides more network structure to create various kinds of innovative real-time applications. One of the essential applications of WSN is target coverage. Forest, agriculture, underwater, terrorism, and other applications have used the target coverage model following its nature. Existing target coverage models are not efficient and continuous, and the application performance is poor. The above-said problem has taken into account, and various earlier research works proposed a different target coverage model, not up to the application requirement. This paper focused on providing an efficient target coverage model for various real-time applications. Thus, a complete, continuous, target coverage model is created for environmental monitoring applications using a novel Termite Flies optimization (TFO) algorithm. Based on the termite fly's movement, distance, targets are covered by optimal sensor nodes. From the experiment, it is found that the proposed TFO algorithm outperforms the existing approaches.
This article proposes a consequent-pole unequal Halbach vernier wheel motor (CPUHVWM), which effectively reduces the self-flux leakage between permanent magnets, and an optimization method is proposed to improve the m...
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This article proposes a consequent-pole unequal Halbach vernier wheel motor (CPUHVWM), which effectively reduces the self-flux leakage between permanent magnets, and an optimization method is proposed to improve the motor optimization efficiency and shorten the optimization time. The key variables are selected by the sensitivity analysis, and a polynomial function suitable for the optimization algorithm is constructed based on the response surface method. Based on the particle swarm optimization algorithm, the weights of different particle search areas are assigned to improve the overall optimization efficiency of the algorithm. Furthermore, to verify the superiority of the performance of the proposed structure and the applicability of the algorithm, the other two similar motor structures are compared, and the algorithm is adopted for the optimization. The optimized electromagnetic characteristics are compared by the finite element method. Finally, the prototype with the optimal parameters is built, and the effectiveness and advantages of the optimization algorithm are verified by experiments.
This paper deals with one of the dire needs of wildlife conservation: effective decision-making frameworks. Growing global connectivity and economic development result in growing demand for certain wildlife products, ...
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Popular methods to deal with computation become strenuous due to the optimization demands that develop more complex nowadays. This research aims to propose a new optimal algorithm, Dove Swarm optimization (DSO), that ...
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Popular methods to deal with computation become strenuous due to the optimization demands that develop more complex nowadays. This research aims to propose a new optimal algorithm, Dove Swarm optimization (DSO), that adopts the foraging behaviors of doves to have six benchmark functions expressing DSO performance. By considering competition for forage, DSO is designed to ensure the most satisfied dove as well as optimization, then compared with 15 popular optimization algorithms using random initial and lattice initial values. The results reveal that DSO performs the best in time efficiency and well in both convergences for these functions in a reasonable region from 1 to 3 seconds, and population diversity for the initialization method from less than 1 second to 9 seconds dependent on the population size. As a result, DSO is indeed a time-efficient and effective algorithm in solving optimization problems.
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